Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory859.4 KiB
Average record size in memory88.0 B

Variable types

Numeric10

Timeseries statistics

Number of series0
Time series length10000
Starting point2020-01-01 00:00:00
Ending point2020-01-01 05:33:18
Period2 seconds
2025-07-22T19:53:00.940593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.965172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Alerts

Channel_0 is highly overall correlated with Channel_1 and 5 other fieldsHigh correlation
Channel_1 is highly overall correlated with Channel_0 and 5 other fieldsHigh correlation
Channel_2 is highly overall correlated with Channel_0 and 5 other fieldsHigh correlation
Channel_4 is highly overall correlated with Channel_0 and 5 other fieldsHigh correlation
Channel_5 is highly overall correlated with Channel_0 and 5 other fieldsHigh correlation
Channel_6 is highly overall correlated with Channel_9High correlation
Channel_7 is highly overall correlated with Channel_0 and 5 other fieldsHigh correlation
Channel_8 is highly overall correlated with Channel_0 and 5 other fieldsHigh correlation
Channel_9 is highly overall correlated with Channel_6High correlation

Reproduction

Analysis started2025-07-22 17:52:55.957603
Analysis finished2025-07-22 17:53:00.936344
Duration4.98 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Channel_0
Real number (ℝ)

High correlation 

Distinct8993
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023315366
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4985
Negative (%)49.9%
Memory size156.2 KiB
2025-07-22T19:53:01.068899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.4881108
median0.10607606
Q37.5150675
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.003178

Descriptive statistics

Standard deviation7.2865773
Coefficient of variation (CV)312.52254
Kurtosis-1.6291142
Mean0.023315366
Median Absolute Deviation (MAD)7.5042853
Skewness-0.0077365137
Sum233.15366
Variance53.094209
MonotonicityNot monotonic
2025-07-22T19:53:01.194109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 495
 
5.0%
10 266
 
2.7%
10 91
 
0.9%
10 80
 
0.8%
10 38
 
0.4%
10 35
 
0.4%
-10 7
 
0.1%
10 2
 
< 0.1%
-10 2
 
< 0.1%
1.232523383 1
 
< 0.1%
Other values (8983) 8983
89.8%
ValueCountFrequency (%)
-10 2
 
< 0.1%
-10 495
5.0%
-10 7
 
0.1%
-9.995997867 1
 
< 0.1%
-9.995929828 1
 
< 0.1%
-9.994626631 1
 
< 0.1%
-9.994386462 1
 
< 0.1%
-9.99403333 1
 
< 0.1%
-9.993015511 1
 
< 0.1%
-9.989771707 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
10 1
 
< 0.1%
10 38
 
0.4%
10 80
 
0.8%
10 266
2.7%
10 91
 
0.9%
10 35
 
0.4%
10 2
 
< 0.1%
9.994467421 1
 
< 0.1%
9.993407077 1
 
< 0.1%

Channel_1
Real number (ℝ)

High correlation 

Distinct8925
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030699536
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4996
Negative (%)50.0%
Memory size156.2 KiB
2025-07-22T19:53:01.249293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.5263141
median0.017815496
Q37.6581726
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.184487

Descriptive statistics

Standard deviation7.3386267
Coefficient of variation (CV)239.04683
Kurtosis-1.6311395
Mean0.030699536
Median Absolute Deviation (MAD)7.5922892
Skewness0.00099129066
Sum306.99536
Variance53.855441
MonotonicityNot monotonic
2025-07-22T19:53:01.305008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 513
 
5.1%
10 280
 
2.8%
10 118
 
1.2%
10 92
 
0.9%
10 39
 
0.4%
10 35
 
0.4%
10 4
 
< 0.1%
-10 2
 
< 0.1%
1.537134004 1
 
< 0.1%
8.920877148 1
 
< 0.1%
Other values (8915) 8915
89.1%
ValueCountFrequency (%)
-10 1
 
< 0.1%
-10 2
 
< 0.1%
-10 1
 
< 0.1%
-10 513
5.1%
-10 1
 
< 0.1%
-10 1
 
< 0.1%
-10 1
 
< 0.1%
-9.998515604 1
 
< 0.1%
-9.99823317 1
 
< 0.1%
-9.997878349 1
 
< 0.1%
ValueCountFrequency (%)
10 4
 
< 0.1%
10 35
 
0.4%
10 92
 
0.9%
10 280
2.8%
10 118
1.2%
10 39
 
0.4%
9.999138987 1
 
< 0.1%
9.995699858 1
 
< 0.1%
9.9951103 1
 
< 0.1%
9.994705254 1
 
< 0.1%

Channel_2
Real number (ℝ)

High correlation 

Distinct8932
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04237048
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4964
Negative (%)49.6%
Memory size156.2 KiB
2025-07-22T19:53:01.363217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.5063703
median0.17721684
Q37.6275827
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.133953

Descriptive statistics

Standard deviation7.3108588
Coefficient of variation (CV)172.54605
Kurtosis-1.6256746
Mean0.04237048
Median Absolute Deviation (MAD)7.5618842
Skewness-0.0059517761
Sum423.7048
Variance53.448657
MonotonicityNot monotonic
2025-07-22T19:53:01.420154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 542
 
5.4%
10 255
 
2.5%
10 96
 
1.0%
10 93
 
0.9%
10 48
 
0.5%
10 38
 
0.4%
-10 2
 
< 0.1%
-10 2
 
< 0.1%
-9.331890919 1
 
< 0.1%
2.24296771 1
 
< 0.1%
Other values (8922) 8922
89.2%
ValueCountFrequency (%)
-10 2
 
< 0.1%
-10 1
 
< 0.1%
-10 542
5.4%
-10 2
 
< 0.1%
-9.993523868 1
 
< 0.1%
-9.992488666 1
 
< 0.1%
-9.991743556 1
 
< 0.1%
-9.986965311 1
 
< 0.1%
-9.986406313 1
 
< 0.1%
-9.985831758 1
 
< 0.1%
ValueCountFrequency (%)
10 38
 
0.4%
10 96
 
1.0%
10 255
2.5%
10 93
 
0.9%
10 48
 
0.5%
10 1
 
< 0.1%
9.998731153 1
 
< 0.1%
9.996752106 1
 
< 0.1%
9.99311652 1
 
< 0.1%
9.991518571 1
 
< 0.1%

Channel_3
Real number (ℝ)

Distinct4023
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.042069351
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4982
Negative (%)49.8%
Memory size156.2 KiB
2025-07-22T19:53:01.476230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-10
median0.14242543
Q310
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.5034891
Coefficient of variation (CV)202.13027
Kurtosis-1.7166579
Mean0.042069351
Median Absolute Deviation (MAD)9.8575746
Skewness-0.0089034492
Sum420.69351
Variance72.309328
MonotonicityNot monotonic
2025-07-22T19:53:01.535488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 2942
29.4%
10 1504
 
15.0%
10 555
 
5.5%
10 516
 
5.2%
10 226
 
2.3%
10 203
 
2.0%
-10 15
 
0.1%
-10 14
 
0.1%
-10 4
 
< 0.1%
10 4
 
< 0.1%
Other values (4013) 4017
40.2%
ValueCountFrequency (%)
-10 4
 
< 0.1%
-10 15
 
0.1%
-10 2942
29.4%
-10 14
 
0.1%
-10 2
 
< 0.1%
-9.999015625 1
 
< 0.1%
-9.998570208 1
 
< 0.1%
-9.987572375 1
 
< 0.1%
-9.976003946 1
 
< 0.1%
-9.967801573 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
10 4
 
< 0.1%
10 226
 
2.3%
10 516
 
5.2%
10 1504
15.0%
10 555
 
5.5%
10 203
 
2.0%
10 4
 
< 0.1%
10 1
 
< 0.1%
9.999917573 1
 
< 0.1%

Channel_4
Real number (ℝ)

High correlation 

Distinct8955
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029431324
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4974
Negative (%)49.7%
Memory size156.2 KiB
2025-07-22T19:53:01.593035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.5597154
median0.11309724
Q37.607191
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.166906

Descriptive statistics

Standard deviation7.3404228
Coefficient of variation (CV)249.40852
Kurtosis-1.6301062
Mean0.029431324
Median Absolute Deviation (MAD)7.5772679
Skewness-0.010289271
Sum294.31324
Variance53.881807
MonotonicityNot monotonic
2025-07-22T19:53:01.648928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 543
 
5.4%
10 246
 
2.5%
10 89
 
0.9%
10 87
 
0.9%
10 44
 
0.4%
10 42
 
0.4%
-8.584808411 1
 
< 0.1%
-6.391310198 1
 
< 0.1%
1.028868679 1
 
< 0.1%
-9.188849958 1
 
< 0.1%
Other values (8945) 8945
89.5%
ValueCountFrequency (%)
-10 1
 
< 0.1%
-10 1
 
< 0.1%
-10 543
5.4%
-10 1
 
< 0.1%
-10 1
 
< 0.1%
-9.999124641 1
 
< 0.1%
-9.996748892 1
 
< 0.1%
-9.99660637 1
 
< 0.1%
-9.991935581 1
 
< 0.1%
-9.990929509 1
 
< 0.1%
ValueCountFrequency (%)
10 44
 
0.4%
10 87
 
0.9%
10 246
2.5%
10 89
 
0.9%
10 42
 
0.4%
10 1
 
< 0.1%
9.998982289 1
 
< 0.1%
9.996265671 1
 
< 0.1%
9.995843719 1
 
< 0.1%
9.995429348 1
 
< 0.1%

Channel_5
Real number (ℝ)

High correlation 

Distinct8894
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046608683
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4991
Negative (%)49.9%
Memory size156.2 KiB
2025-07-22T19:53:01.707129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.4814375
median0.042498889
Q37.593812
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.075249

Descriptive statistics

Standard deviation7.3050187
Coefficient of variation (CV)156.73085
Kurtosis-1.6231838
Mean0.046608683
Median Absolute Deviation (MAD)7.5418132
Skewness-0.0080302114
Sum466.08683
Variance53.363298
MonotonicityNot monotonic
2025-07-22T19:53:01.763032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 538
 
5.4%
10 302
 
3.0%
10 98
 
1.0%
10 95
 
0.9%
10 38
 
0.4%
10 37
 
0.4%
-10 3
 
< 0.1%
-10 2
 
< 0.1%
-10 2
 
< 0.1%
-9.421470941 1
 
< 0.1%
Other values (8884) 8884
88.8%
ValueCountFrequency (%)
-10 1
 
< 0.1%
-10 3
 
< 0.1%
-10 538
5.4%
-10 2
 
< 0.1%
-10 2
 
< 0.1%
-9.995255003 1
 
< 0.1%
-9.992850718 1
 
< 0.1%
-9.992806388 1
 
< 0.1%
-9.992694769 1
 
< 0.1%
-9.989864033 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
10 37
 
0.4%
10 98
 
1.0%
10 302
3.0%
10 95
 
0.9%
10 38
 
0.4%
10 1
 
< 0.1%
9.999816875 1
 
< 0.1%
9.998129738 1
 
< 0.1%
9.993732185 1
 
< 0.1%

Channel_6
Real number (ℝ)

High correlation 

Distinct6769
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.032515199
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative5013
Negative (%)50.1%
Memory size156.2 KiB
2025-07-22T19:53:01.819397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-8.7356066
median-0.031712242
Q38.7864999
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)17.522106

Descriptive statistics

Standard deviation8.0430049
Coefficient of variation (CV)-247.36139
Kurtosis-1.6860818
Mean-0.032515199
Median Absolute Deviation (MAD)8.7777461
Skewness0.0075456955
Sum-325.15199
Variance64.689928
MonotonicityNot monotonic
2025-07-22T19:53:01.874663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 1590
 
15.9%
10 829
 
8.3%
10 296
 
3.0%
10 284
 
2.8%
10 114
 
1.1%
10 106
 
1.1%
-10 12
 
0.1%
-10 4
 
< 0.1%
-10 3
 
< 0.1%
10 2
 
< 0.1%
Other values (6759) 6760
67.6%
ValueCountFrequency (%)
-10 2
 
< 0.1%
-10 4
 
< 0.1%
-10 1590
15.9%
-10 12
 
0.1%
-10 3
 
< 0.1%
-9.998500013 1
 
< 0.1%
-9.996546595 1
 
< 0.1%
-9.995138911 1
 
< 0.1%
-9.994688542 1
 
< 0.1%
-9.99161671 1
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
10 106
 
1.1%
10 296
 
3.0%
10 829
8.3%
10 284
 
2.8%
10 114
 
1.1%
10 1
 
< 0.1%
9.999664215 1
 
< 0.1%
9.999250395 1
 
< 0.1%
9.999079773 1
 
< 0.1%

Channel_7
Real number (ℝ)

High correlation 

Distinct8934
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015888731
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4978
Negative (%)49.8%
Memory size156.2 KiB
2025-07-22T19:53:01.929750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.5984995
median0.10919286
Q37.5990121
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.197512

Descriptive statistics

Standard deviation7.3494983
Coefficient of variation (CV)462.56042
Kurtosis-1.6359696
Mean0.015888731
Median Absolute Deviation (MAD)7.6021155
Skewness-0.012104834
Sum158.88731
Variance54.015125
MonotonicityNot monotonic
2025-07-22T19:53:01.984804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 558
 
5.6%
10 267
 
2.7%
10 100
 
1.0%
10 78
 
0.8%
10 34
 
0.3%
10 31
 
0.3%
-10 3
 
< 0.1%
10 2
 
< 0.1%
-10 2
 
< 0.1%
-6.006098193 1
 
< 0.1%
Other values (8924) 8924
89.2%
ValueCountFrequency (%)
-10 1
 
< 0.1%
-10 3
 
< 0.1%
-10 558
5.6%
-10 1
 
< 0.1%
-10 2
 
< 0.1%
-9.999905785 1
 
< 0.1%
-9.998398824 1
 
< 0.1%
-9.996858904 1
 
< 0.1%
-9.993044128 1
 
< 0.1%
-9.991250528 1
 
< 0.1%
ValueCountFrequency (%)
10 31
 
0.3%
10 100
 
1.0%
10 267
2.7%
10 78
 
0.8%
10 34
 
0.3%
10 2
 
< 0.1%
9.999994829 1
 
< 0.1%
9.998297506 1
 
< 0.1%
9.997163007 1
 
< 0.1%
9.995079702 1
 
< 0.1%

Channel_8
Real number (ℝ)

High correlation 

Distinct8970
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039783348
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative4974
Negative (%)49.7%
Memory size156.2 KiB
2025-07-22T19:53:02.044768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-7.5631797
median0.14453496
Q37.5992894
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)15.162469

Descriptive statistics

Standard deviation7.3226333
Coefficient of variation (CV)184.06277
Kurtosis-1.6285711
Mean0.039783348
Median Absolute Deviation (MAD)7.5770385
Skewness-0.0096032738
Sum397.83348
Variance53.620958
MonotonicityNot monotonic
2025-07-22T19:53:02.164675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 519
 
5.2%
10 270
 
2.7%
10 89
 
0.9%
10 87
 
0.9%
10 37
 
0.4%
10 33
 
0.3%
-10 2
 
< 0.1%
-9.393687463 1
 
< 0.1%
-9.315252493 1
 
< 0.1%
7.356816296 1
 
< 0.1%
Other values (8960) 8960
89.6%
ValueCountFrequency (%)
-10 1
 
< 0.1%
-10 519
5.2%
-10 2
 
< 0.1%
-10 1
 
< 0.1%
-9.997656701 1
 
< 0.1%
-9.995309014 1
 
< 0.1%
-9.9943809 1
 
< 0.1%
-9.993829064 1
 
< 0.1%
-9.991449377 1
 
< 0.1%
-9.989675318 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
10 37
 
0.4%
10 87
 
0.9%
10 270
2.7%
10 89
 
0.9%
10 33
 
0.3%
10 1
 
< 0.1%
9.997849063 1
 
< 0.1%
9.995353775 1
 
< 0.1%
9.992685077 1
 
< 0.1%

Channel_9
Real number (ℝ)

High correlation 

Distinct6722
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03711725
Minimum-10
Maximum10
Zeros0
Zeros (%)0.0%
Negative5031
Negative (%)50.3%
Memory size156.2 KiB
2025-07-22T19:53:02.218874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-10
Q1-8.785818
median-0.13061288
Q38.7290196
95-th percentile10
Maximum10
Range20
Interquartile range (IQR)17.514838

Descriptive statistics

Standard deviation8.0534787
Coefficient of variation (CV)-216.97402
Kurtosis-1.686699
Mean-0.03711725
Median Absolute Deviation (MAD)8.7587446
Skewness0.0062403456
Sum-371.1725
Variance64.85852
MonotonicityNot monotonic
2025-07-22T19:53:02.275458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 1633
 
16.3%
10 797
 
8.0%
10 311
 
3.1%
10 290
 
2.9%
10 119
 
1.2%
10 110
 
1.1%
-10 14
 
0.1%
-10 10
 
0.1%
10 2
 
< 0.1%
-10 2
 
< 0.1%
Other values (6712) 6712
67.1%
ValueCountFrequency (%)
-10 2
 
< 0.1%
-10 14
 
0.1%
-10 1633
16.3%
-10 10
 
0.1%
-10 1
 
< 0.1%
-9.994031989 1
 
< 0.1%
-9.992999317 1
 
< 0.1%
-9.99211967 1
 
< 0.1%
-9.991929263 1
 
< 0.1%
-9.988884488 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
10 119
 
1.2%
10 311
 
3.1%
10 797
8.0%
10 290
 
2.9%
10 110
 
1.1%
10 2
 
< 0.1%
9.999400525 1
 
< 0.1%
9.998401798 1
 
< 0.1%
9.997496384 1
 
< 0.1%

Interactions

2025-07-22T19:53:00.361014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.145827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.598512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.101880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.546231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.987083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.500204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.947923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.404417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.914243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.406660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.197012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.643141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.146847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.588165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.031273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.544514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.993009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-22T19:53:00.450636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.241511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.688015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.189158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.633584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-22T19:53:00.494932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.288098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.731760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.233907image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.677206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.119865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.635212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.085484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.537234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.047147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.539411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.331980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.775079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.277279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.721424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.165985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.681629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.132618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.581398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.092484image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.584123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.376957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.818204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.321317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.765083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.209099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.725255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.178868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.625600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.136207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-22T19:52:57.364668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.808825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.254550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.769273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.223919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.670250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.180407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.672758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-22T19:52:58.857471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.315275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.758770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.272042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.761314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:56.553144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.057844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.501109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:57.941406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.450546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:58.902531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.360592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:52:59.871218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-22T19:53:00.316727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-22T19:53:02.318618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Channel_0Channel_1Channel_2Channel_3Channel_4Channel_5Channel_6Channel_7Channel_8Channel_9
Channel_01.0000.8560.860-0.4230.8560.860-0.4630.8580.855-0.459
Channel_10.8561.0000.856-0.4270.8580.858-0.4570.8560.857-0.456
Channel_20.8600.8561.000-0.4250.8550.856-0.4630.8550.854-0.455
Channel_3-0.423-0.427-0.4251.000-0.427-0.424-0.352-0.426-0.423-0.357
Channel_40.8560.8580.855-0.4271.0000.856-0.4570.8550.854-0.457
Channel_50.8600.8580.856-0.4240.8561.000-0.4630.8600.857-0.457
Channel_6-0.463-0.457-0.463-0.352-0.457-0.4631.000-0.462-0.4590.816
Channel_70.8580.8560.855-0.4260.8550.860-0.4621.0000.861-0.462
Channel_80.8550.8570.854-0.4230.8540.857-0.4590.8611.000-0.455
Channel_9-0.459-0.456-0.455-0.357-0.457-0.4570.816-0.462-0.4551.000

Missing values

2025-07-22T19:53:00.821623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-22T19:53:00.890368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

colsChannel_0Channel_1Channel_2Channel_3Channel_4Channel_5Channel_6Channel_7Channel_8Channel_9
date
2020-01-01 00:00:00-3.5022826.4984749.303281-4.492846-5.247729-10.000000-0.185594-1.254006-0.46823210.000000
2020-01-01 00:00:02-8.3616741.445274-3.976604-3.7893937.281969-10.0000006.484769-7.95134310.0000000.464479
2020-01-01 00:00:04-4.3367439.387145-7.2249628.937463-10.000000-3.45665210.000000-0.4580210.5946728.191028
2020-01-01 00:00:06-8.960544-1.493426-10.0000007.129725-6.411168-8.16286210.000000-8.601640-2.967463-6.059203
2020-01-01 00:00:081.055208-6.1887473.96268010.000000-10.0000004.3074530.925952-6.131798-2.672101-4.202614
2020-01-01 00:00:100.6902085.14617510.000000-6.1063366.720332-2.188129-7.762917-0.291518-10.000000-4.417446
2020-01-01 00:00:1210.000000-9.063624-10.000000-0.027209-7.299486-5.0332202.731448-3.249396-2.2256125.078785
2020-01-01 00:00:143.045774-10.000000-3.6083252.517417-2.298254-4.60556410.0000006.724583-2.872659-3.122953
2020-01-01 00:00:16-2.559417-7.088679-5.777390-10.000000-7.244992-6.37014110.000000-9.776305-6.1438057.566829
2020-01-01 00:00:185.6722268.9562116.030192-10.0000005.2027126.36844110.0000004.4299048.0054445.702793
colsChannel_0Channel_1Channel_2Channel_3Channel_4Channel_5Channel_6Channel_7Channel_8Channel_9
date
2020-01-01 05:33:007.7249826.8286266.617924-10.0000007.19349810.000000-7.3450447.7768797.741192-5.488993
2020-01-01 05:33:028.4824279.32864210.000000-10.0000009.3428728.263293-1.4197527.5128169.859695-1.928835
2020-01-01 05:33:04-6.810729-10.000000-7.4631617.658487-8.702026-6.9360829.791200-7.737863-6.84801410.000000
2020-01-01 05:33:06-4.745913-7.291802-7.72051910.000000-10.000000-6.0056256.421953-9.578216-8.8773537.664612
2020-01-01 05:33:087.66616810.0000009.432333-10.0000007.3481548.8375489.7719947.0967587.5565739.727551
2020-01-01 05:33:109.1810379.94952610.000000-10.0000008.1981887.0169120.4303214.9378937.385373-0.264961
2020-01-01 05:33:126.2111475.8457404.943193-10.0000003.2277053.2342216.5973302.3921024.22977810.000000
2020-01-01 05:33:14-6.742547-6.949797-7.828166-10.000000-7.557412-6.43130710.000000-6.569968-6.6858549.829434
2020-01-01 05:33:165.7185296.8670705.570893-10.0000007.4626235.8641839.23996610.0000007.4457584.632382
2020-01-01 05:33:18-4.775855-0.487844-4.63208310.000000-6.416399-8.5272898.982562-10.000000-7.4267965.074506